Statistical methods for characterizing patients who highly-benefit from treatments and programs in Alzheimers, HIV, and other heterogeneous diseases

用于描述从阿尔茨海默病、艾滋病毒和其他异质性疾病的治疗和项目中受益匪浅的患者的统计方法

• 批准号: 9919323
• 负责人: CONSTANTINE E FRANGAKIS
• 金额: $ 44.45万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-04 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9919323
• 关键词: AIDS/HIV problem; Acquired Immunodeficiency Syndrome; Address; Agitation; Alzheimer' s Disease; American; Area; Benefits and Risks; Citalopram; Clinical; Data; Dementia; Disease; Emergency Situation; Equilibrium; Feedback; Goals; HIV; Heterogeneity; Human; Intervention; Link; Methods; Modeling; Observational Study; Patients; Physicians; Placebos; Randomized; Randomized Controlled Trials; Resources; Serious Adverse Event; Site; Statistical Methods; Symptoms; Testing; Uncertainty; Vietnam; Work; adverse event risk; antiretroviral therapy; base; comorbidity; cost; disease heterogeneity; effective intervention; high risk; intervention effect; patient subsets; profiles in patients; programs; randomized trial; standard care; treatment program; uptake

Project Summary Accurate characterization of which patients benefit highly from a treatment or program in Alzheimer's or HIV dis- eases are central for knowing which treatments work for which patients, and to plan effectively for the others. A major challenge for this is heterogeneity of these diseases. Until now, treatment studies for Alzheimer's disease with comorbidities have shown little if any efficacy. Also, for HIV/AIDS patients in resource -constrained settings, only a small fraction use antiretroviral treatment (ART) or benefit from a given program to increase ART uptake. Standard methods to characterize which patients benefit from such treatments/programs, first construct a predic- tor using standard statistical criteria, and then use that predictor to characterize high-benefit patients. For such methods, therefore, the clinical goal – to characterize high-benefit patients – is considered only at the implemen- tation stage, and is not used for the construction of the method. In earlier work, we have shown that such methods can dramatically misrepresent high-benefit patients; and we have developed a type of method that directly links the clinical goal (high benefit) into the construction of the characterization mechanism. We were motivated by: a study to reduce agitation in patients with Alzheimer's disease; and a study to increase ART uptake among HIV patients in Vietnam. We have shown that methods that lack this clinical link can miss and underestimate high benefit patients by a factor of 2 or more, compared to even simple methods of this new type. In this proposal, we will develop and apply such new clinically-targeted statistical methods for characterizing high-benefit patients. Such methods will allow physicians and patients to make better choices of best treatments and programs, with potential to benefit millions of patients. The proposed methods are developed for three aims, and, following the preliminary work, are motivated by and will be applied to Alzheimer's and HIV studies. Aim 1. Develop methods to characterize patients who highly benefit from treatment in randomized con- trolled trials. These methods are significant because they can identify high benefit patients who would be missed when using standard methods. Aim 2. Develop methods to characterize patients with high benefit and high risk in randomized trials. Here, we will develop methods to characterize, patients with high benefit, among those with high risk of an adverse event. These methods can allow patients to better balance risk and benefit of treatments. Aim 3. Develop methods to characterize patients who highly benefit from treatment in observational studies. We will use methods to transform observational studies to a study as close as possible to a randomized one, where we can then extend the methods of aims 1 and 2. These methods are significant where randomized trials are not easy to conduct. They will be tested using the above two studies, and also at a PEPFAR (President's Emergency Program for AIDS Relief) site, to characterize patients who receive low benefit from the program, in order to provide to them extra support.

项目摘要 精确表征哪些患者在阿尔茨海默氏症或HIV疾病中的治疗或计划中受益匪浅 心灵是了解哪些治疗方法的核心，并为其他患者有效计划。 这是这些疾病的异质性的主要挑战。到目前为止，阿尔茨海默氏病的治疗研究 合并症几乎没有任何效率。同样，对于艾滋病毒/艾滋病患者，在资源约束的环境中 只有一小部分使用给定程序的抗逆转录病毒治疗（ART）或受益，以增加ART吸收。 表征哪些患者从此类治疗/程序中受益的标准方法，首先构建了一种预性 使用标准统计标准，然后使用该预测因子来表征高纤维拟合患者。为此 因此，方法仅在企业中考虑了临床目标 - 表征高纤维拟合患者的方法 阶段，不用于构建该方法。在较早的工作中，我们已经证明了这样的方法 可能会大幅度陈述的高效应患者；我们已经开发了一种直接链接的方法 临床目标（高收益）用于构建表征机制。我们的动力是： 研究以减少阿尔茨海默氏病患者的躁动；以及一项增加艾滋病毒中艺术吸收的研究 越南的患者。我们已经表明，缺乏这种临床链接的方法可能会错过并低估了高度 与这种新型类型的简单方法相比，相比之下，受益患者的良好型患者的成绩为2倍或更多。 在此建议中，我们将开发并应用这种新的临床统计方法来表征 高效应患者。这样的方法将使医生和患者可以更好地选择最佳治疗方法 和计划，有可能受益数百万患者。提出的方法是针对三个目标开发的 并且，在初步工作之后，由阿尔茨海默氏症和艾滋病毒研究应用于动机。 目标1。开发方法来表征那些在随机伴奏中受到治疗高度好处的患者 拖钓试验。这些方法是显着的 使用标准方法时。 AIM 2。开发方法来表征随机试验中具有高收益和高风险的患者。 在这里，我们将开发出表征高收益的患者的表征的方法， 不利事件。这些方法可以使患者更好地平衡风险和治疗的好处。 目标3。开发方法来表征观察性治疗中高度好处的患者 研究。我们将使用方法将观察性研究转化为尽可能近的研究 一个，然后我们可以扩展目标1和2的方法。这些方法在随机的地方显着 试验不容易进行。他们将使用以上两项研究对他们进行测试，也将在Pepfar（总统的 艾滋病救济的紧急计划）网站，以表征从该计划中获得低益处的患者 为了向他们提供额外的支持。